Dependence of tropical cyclone weakening rate in response to an imposed moderate environmental vertical wind shear on the warm-core strength and height of the initial vortex

This study investigated the dependence of the early tropical cyclone (TC) weakening rate in response to an imposed moderate environmental vertical wind shear (VWS) on the warm-core strength and height of the TC vortex using idealized numerical simulations. Results show that the weakening of the warm core by upper-level ventilation is the primary factor leading to the early TC weakening in response to an imposed environmental VWS. The upper-level ventilation is dominated by eddy radial advection of the warm-core air. The TC weakening rate is roughly proportional to the warm-core strength and height of the initial TC vortex. The boundary-layer ventilation shows no relationship with the early weakening rate of the TC in response to an imposed moderate VWS. The findings suggest that some previous diverse results regarding the TC weakening in environmental VWS could be partly due to the different warm-core strengths and heights of the initial TC vortex.

Methods
The original data generated by our idealized experiments using the WRF model is very large, so we used Fortran (you can also use Python, MATLAB and other tools) to preprocess the data and obtain the main variables needed for our research analysis. The preprocessed data is in binary format.

The WRF model is a numerical weather prediction and atmospheric research model developed by organizations including the National Center for Atmospheric Research (NCAR) and the National Centers for Environmental Prediction (NCEP) in the USA. WRF is open-source software and can be downloaded from https://github.com/wrf-model/WRF/releases.

The specific parameters and settings used to configure the WRF model runs are described in detail in the paper. Interested researchers can follow the settings in the paper to regenerate the original raw data. However, the raw data files are very large (tens of GB per file), making direct analysis difficult. Therefore, we used tools like Fortran to preprocess the raw data into smaller binary files containing the key variables needed for analysis, such as potential temperature, etc. The binary files are around a few hundred MB in size. We strongly recommend that subsequent researchers directly use these preprocessed binary data files, which will greatly simplify the data processing workflow.